University of Nottingham

About: University of Nottingham is a education organization based out in Nottingham, Nottingham, United Kingdom. It is known for research contribution in the topics: Population & Context (language use). The organization has 54772 authors who have published 119600 publications receiving 4227408 citations. The organization is also known as: The University of Nottingham & University College, Nottingham.

The Regulation of 1-Aminocyclopropane-1-Carboxylic Acid Synthase Gene Expression during the Transition from System-1 to System-2 Ethylene Synthesis in Tomato

Abstract: 1-Aminocyclopropane-1-carboxylic acid synthase (ACS) is one of the key regulatory enzymes involved in the synthesis of the hormone ethylene and is encoded by a multigene family containing at least eight members in tomato (Lycopersicon esculentum). Increased ethylene production accompanies ripening in tomato, and this coincides with a change in the regulation of ethylene synthesis from auto-inhibitory to autostimulatory. The signaling pathways that operate to bring about this transition from so-called system-1 to system-2 ethylene production are unknown, and we have begun to address these by investigating the regulation of ACS expression during ripening. Transcripts corresponding to four ACS genes, LEACS1A, LEACS2, LEACS4, and LEACS6, were detected in tomato fruit, and expression analysis using the ripening inhibitor (rin) mutant in combination with ethylene treatments and the Never-ripe (Nr) mutant has demonstrated that each is regulated in a unique way. A proposed model suggests that system-1 ethylene is regulated by the expression of LEACS1A and LEACS6. In fruit a transition period occurs in which the RIN gene plays a pivotal role leading to increased expression of LEACS1A and induction of LEACS4. System-2 ethylene synthesis is subsequently initiated and maintained by ethylene-dependent induction of LEACS2.

Self-organization of the human embryo in the absence of maternal tissues

Abstract: Zernicka-Goetz and colleagues report an in vitro culture system that recapitulates hallmarks of human embryo morphogenesis before gastrulation, including formation of the pro-amniotic cavity and appearance of the prospective yolk sac.

A catalogue of the Chandra Deep Field South with multi-colour classification and photometric redshifts from COMBO-17

Abstract: We present the COMBO-17 object catalogue of the Chandra Deep Field South for public use, covering a field which is in size. This catalogue lists astrometry, photometry in 17 passbands from 350 to 930 nm, and ground-based morphological data for 63 501 objects. The catalogue also contains multi-colour classification into the categories Star , Galaxy and Quasar as well as photometric redshifts. We include restframe luminosities in Johnson, SDSS and Bessell passbands and estimated errors. The redshifts are most reliable at , where the sample contains approximately 100 quasars, 1000 stars and 10 000 galaxies. We use nearly 1000 spectroscopically identified objects in conjunction with detailed simulations to characterize the performance of COMBO-17. We show that the selection of quasars , more generally type-1 AGN, is nearly complete and minimally contaminated at for luminosities above MB = -21.7. Their photometric redshifts are accurate to roughly 5000 km s-1 . Galaxy redshifts are accurate to 1% in at . They degrade in quality for progressively fainter galaxies, reaching accuracies of 2% for galaxies with and of 10% for galaxies with . The selection of stars is complete to , and deeper for M stars. We also present an updated discussion of our classification technique with maps of survey completeness, and discuss possible failures of the statistical classification in the faint regime at .

Human Pose Estimation via Convolutional Part Heatmap Regression

Abstract: This paper is on human pose estimation using Convolutional Neural Networks. Our main contribution is a CNN cascaded architecture specifically designed for learning part relationships and spatial context, and robustly inferring pose even for the case of severe part occlusions. To this end, we propose a detection-followed-by-regression CNN cascade. The first part of our cascade outputs part detection heatmaps and the second part performs regression on these heatmaps. The benefits of the proposed architecture are multi-fold: It guides the network where to focus in the image and effectively encodes part constraints and context. More importantly, it can effectively cope with occlusions because part detection heatmaps for occluded parts provide low confidence scores which subsequently guide the regression part of our network to rely on contextual information in order to predict the location of these parts. Additionally, we show that the proposed cascade is flexible enough to readily allow the integration of various CNN architectures for both detection and regression, including recent ones based on residual learning. Finally, we illustrate that our cascade achieves top performance on the MPII and LSP data sets. Code can be downloaded from http://www.cs.nott.ac.uk/~psxab5/.